Explaining recommender systems fairness and accuracy through the lens of data characteristics

Author:

Deldjoo YasharORCID,Bellogin AlejandroORCID,Di Noia Tommaso

Funder

Horizon 2020 Framework Programme

H2020

Ministerio de Ciencia, Innovación y Universidades

Ministero dell’Istruzione, dell’Università e della Ricerca

Publisher

Elsevier BV

Subject

Library and Information Sciences,Management Science and Operations Research,Computer Science Applications,Media Technology,Information Systems

Reference76 articles.

1. Multistakeholder recommender systems;Abdollahpouri,2021

2. Managing popularity bias in recommender systems with personalized re-ranking;Abdollahpouri,2019

3. Session-based hotel recommendations dataset: As part of the ACM recommender system challenge 2019;Adamczak;ACM Transactions on Intelligent Systems and Technology (TIST),2020

4. Impact of data characteristics on recommender systems performance;Adomavicius;ACM Transactions on Management Information Systems,2012

5. ValuePick: Towards a value-oriented dual-goal recommender system;Akoglu,2010

Cited by 43 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Characteristics of the Learning Data of a Session-Based Recommendation System and their Impact on the Performance of the System;International Conference on Information Systems Development;2024-09-09

2. Understanding Biases in ChatGPT-based Recommender Systems: Provider Fairness, Temporal Stability, and Recency;ACM Transactions on Recommender Systems;2024-08-28

3. From Variability to Stability: Advancing RecSys Benchmarking Practices;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

4. International Workshop on Algorithmic Bias in Search and Recommendation (BIAS);Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval;2024-07-10

5. Hypergraph Convolutional Network for User-Oriented Fairness in Recommender Systems;Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval;2024-07-10

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3